Mechanisms of Asian Summer Monsoon Changes in Response to Anthropogenic Forcing in CMIP5 Models

Changes of the Asian summer monsoon in response to anthropogenic forcing are examined using observations and phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel, multirealization ensemble. In the twentieth century, CMIP5 models indicate a predominantly drying Asian monsoon, while in the twenty-first century under the representative concentration pathway 8.5 (RCP8.5) scenario, monsoon rainfall enhances across the entire Asian domain. The thermodynamic and dynamic mechanisms causing the changes are evaluated using specific humidity and winds, as well as the moisture budget. The drying trend in the CMIP5 historical simulations and the wetting trend in the RCP8.5 projections can be explained by the relative importance of dynamic and thermodynamic contributions to the total mean moisture convergence. While the thermodynamic mechanism dominates in the future, the historical rainfall changes are dominated by the changes in circulation. The relative contributions of aerosols and greenhouse gases (GHGs) on the historical monsoon change are further examined using CMIP5 single-forcing simulations. Rainfall reduces under aerosol forcing and increases under GHG forcing. Aerosol forcing dominates over the greenhouse effect during the historical period, leading to the general drying trend in the all-forcing simulations. While the thermodynamic change of mean moisture convergence in the all-forcing case is dominated by the GHG forcing, the dynamic change of mean moisture convergence in the all-forcing case is dominated by the aerosol forcing

Robust features of Atlantic multi-decadal variability and its climate impacts

Atlantic Multi-decadal Variability (AMV), also known as the Atlantic Multi-decadal Oscillation (AMO), is characterized by a sharp rise and fall of the North Atlantic basin-wide sea surface temperatures (SST) on multi-decadal time scales.Widespread consequences of these rapid temperature swings were noted in many previous studies. Among these are the drying of Sahel in the 1960-70s and change in the frequency and intensity of Atlantic hurricanes on multi-decadal time scales. Given the short instrumental data records (about a century long) the central question is whether these climate fluctuations are robustly linked with the AMV and to what extent are these connections subject to changes in a changing climate. Here we address this issue by using the CMIP3 simulations for the 20th, 21st, and pre-industrial eras with 23 IPCC models. While models tend to produce AMV of shorter time scales and less periodic than suggested by the observations, the spatial structures of the SST anomaly patterns, and their association with worldwide precipitation, are surprisingly similar between models (with differing external forcing) and observations. Our results confirm the strong link between AMV and Sahel rainfall and suggest a clear physical mechanism for the linkage in terms of meridional shifts of the Atlantic ITCZ. The results also help clarify influences that may not be robust, such as the impacts over North America, India, and Australia

Natural and Forced North Atlantic Hurricane Potential Intensity Change in CMIP5 Models

Possible future changes of North Atlantic hurricane intensity and the attribution of past hurricane intensity changes in the historical period are investigated using phase 5 of the Climate Model Intercomparison Project (CMIP5), multimodel, multiensemble simulations. For this purpose, the potential intensity (PI), the theoretical upper limit of the tropical cyclone intensity given the large-scale environment, is used. The CMIP5 models indicate that the PI change as a function of sea surface temperature (SST) variations associated with the Atlantic multidecadal variability (AMV) is more effective than that associated with climate change. Thus, relatively small changes in SST due to natural multidecadal variability can lead to large changes in PI, and the model-simulated multidecadal PI change during the historical period has been largely dominated by AMV. That said, the multimodel mean PI for the Atlantic main development region shows a significant increase toward the end of the twenty-first century under both the RCP4.5 and RCP8.5 emission scenarios. This is because of enhanced surface warming, which would place the North Atlantic PI largely above the historical mean by the mid-twenty-first century, based on CMIP5 model projection. The authors further attribute the historical PI changes to aerosols and greenhouse gas (GHG) forcing using CMIP5 historical single-forcing simulations. The model simulations indicate that aerosol forcing has been more effective in causing PI changes than the corresponding GHG forcing; the decrease in PI due to aerosols and increase due to GHG largely cancel each other. Thus, PI increases in the recent 30 years appears to be dominated by multidecadal natural variability associated with the positive phase of the AMV

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}

Past and Future Hurricane Intensity Change along the U.S. East Coast

The ocean and atmosphere in the North Atlantic are coupled through a feedback mechanism that excites a dipole pattern in vertical wind shear (VWS), a metric that strongly controls Atlantic hurricanes. In particular, when tropical VWS is under the weakening phase and thus favorable for increased hurricane activity in the Main Development Region (MDR), a protective barrier of high VWS inhibits hurricane intensification along the U.S. East Coast. Here we show that this pattern is driven mostly by natural decadal variability, but that greenhouse gas (GHG) forcing erodes the pattern and degrades the natural barrier along the U.S. coast. Twenty-first century climate model projections show that the increased VWS along the U.S. East Coast during decadal periods of enhanced hurricane activity is substantially reduced by GHG forcing, which allows hurricanes approaching the U.S. coast to intensify more rapidly. The erosion of this natural intensification barrier is especially large following the Representative Concentration Pathway 8.5 (rcp8.5) emission scenario